Publications by authors named "A P Toropova"

148 Publications

Can the Monte Carlo method predict the toxicity of binary mixtures?

Environ Sci Pollut Res Int 2021 Mar 23. Epub 2021 Mar 23.

Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy.

Risk assessment of toxicants mainly is a result of experiments with single substances. However, toxicity in natural ecosystems typically does not result from single toxicant exposure but is rather a result of exposure to mixtures of toxicants. It is not surprising a mixture of toxicity is a subject of eco-toxicological interest for several decades. A quantitative structure-activity relationships (QSAR)-based approach is an attractive approach to assessing the joint effects in the binary mixtures. The validity of the proposed approach was demonstrated by comparing the predicted values against the experimentally determined values. Simplified molecular input-line entry system (SMILES) is used for the representation of the molecular structures of components of two-component mixtures to build up QSAR. The SMILES-based models are improving if the Monte Carlo optimization aimed to define 2D-optimal descriptors apply the so-called index of ideality of correlation (IIC), which is a mathematical function of both the correlation coefficient and mean absolute error calculated for the positive and negative difference between observed and calculated values of toxicity. The average statistical quality of these models (for the validation set) is n=25, R=0.95, and RMSE=0.375.
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http://dx.doi.org/10.1007/s11356-021-13460-1DOI Listing
March 2021

Quasi-SMILES as a basis for the development of models for the toxicity of ZnO nanoparticles.

Sci Total Environ 2021 Jun 1;772:145532. Epub 2021 Feb 1.

Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy. Electronic address:

The application of nanomaterials is expanding. Therefore, it is necessary to investigate the relationship between the structure and toxicity of different nanomaterials. Quasi-SMILES is a line of symbols which are codes of corresponding conditions of experiments aimed to estimate the toxicity of ZnO nanoparticles towards the rat via intraperitoneal injections. By means of the Monte Carlo method, the so-called correlation weights for fragments of quasi-SMILES can be calculated. Having the numerical data on the correlation weights one can build up a one-variable model for the toxicity. The checking up of the approach with five random splits of all available data on results of thirty-six experiments into a sub-system of training and sub-system of validation has confirmed the significance of the statistical quality of models obtained with the above approach. The average determination coefficient equal to 0.957 (dispersion 0.010) and average root mean square error equal to 7.25 [mg/kg] (dispersion 0.59 [mg/kg]).
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http://dx.doi.org/10.1016/j.scitotenv.2021.145532DOI Listing
June 2021

Paradox of 'ideal correlations': improved model for air half-life of persistent organic pollutants.

Environ Technol 2021 Feb 11:1-6. Epub 2021 Feb 11.

Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.

The persistence of organic pollutants is an important environmental property due to the extended possibility to have an impact of corresponding substances. In many cases, the experimental values of the thousands of contaminants are missing. The object of the study is novel computational modelling for air pollutions. Quantitative structure-property relationship (QSPR) for air half-life has been built using the Monte Carlo method with applying the index of ideality of correlation (). The basis of the predictive model of air half-life is the representation of the molecular structure by simplifying molecular input-line entry system (SMILES) and numerical data on the above endpoint (expressed by hours) converted to a decimal logarithm. The statistical quality of the model has been checked up with different validation metrics and is quite good. Paradoxically, the improvement of the statistical quality via the for the validation set is done in detriment to the training set. The new model has performed better than those obtained previously on the same set of compounds, for the prediction of new compounds in the validation set. Some semi-quantitative indicators for the mechanistic interpretation of the model are suggested.
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http://dx.doi.org/10.1080/09593330.2021.1882588DOI Listing
February 2021

The sequence of amino acids as the basis for the model of biological activity of peptides.

Theor Chem Acc 2021 22;140(2):15. Epub 2021 Jan 22.

Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy.

The algorithm of building up a model for the biological activity of peptides as a mathematical function of a sequence of amino acids is suggested. The general scheme is the following: The total set of available data is distributed into the active training set, passive training set, calibration set, and validation set. The training (both active and passive) and calibration sets are a system of generation of a model of biological activity where each amino acid obtains special correlation weight. The numerical data on the correlation weights calculated by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral). The target function aimed to give the best result for the calibration set (not for the training set). The final checkup of the model is carried out with data on the validation set (peptides, which are not visible during the creation of the model). Described computational experiments confirm the ability of the approach to be a tool for the design of predictive models for the biological activity of peptides (expressed by pIC50).
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http://dx.doi.org/10.1007/s00214-020-02707-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7820519PMC
January 2021

The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: A pun or a reliable law?

Toxicol Lett 2021 Apr 20;340:133-140. Epub 2021 Jan 20.

Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy. Electronic address:

Some new products, which include common personal-care products, drugs, household items, can be hazardous in aspect personal care products/cosmetics and their ingredients (i.e. the above can effect human skin). International organizations (e.g. the Organisation for Economic Co-operation and Development-OECD) recommend evaluating individual ingredients when assessing the safety of personal care or cosmetic products. Thus, checking up that "popular at the market" substances are non-toxic, do not penetrate into or through normal or compromised human skin, and therefore, pose no risk to human health is an essential element of modern toxicology. The development of reliable models of toxicological endpoints is a tool to carry out the above checking up via quantitative structure-activity relationships (QSARs). The reliability of the QSAR is the current task of mathematical statistics. Recently, the index of ideality of correlation (IIC) and correlation intensity index (CII) were suggested as criteria of predictive potential (i.e. reliability) of QSAR-models. Here, the abilities of these criteria were studied for the case of building up models for skin sensitivity (LLNA, local lymph node assay). Computational experiments have confirmed that the IIC demonstrates an obvious ability to improve the predictive potential of models of skin sensitization. The applying of the CII for the case of skin sensitization also improves the quality of the model. However, the best models for skin sensitization were observed if the above-mentioned criteria are applied jointly (n = 268; R = 0.60; RMSE = 0.63).
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http://dx.doi.org/10.1016/j.toxlet.2021.01.015DOI Listing
April 2021

EFSA's OpenFoodTox: An open source toxicological database on chemicals in food and feed and its future developments.

Environ Int 2021 Jan 8;146:106293. Epub 2020 Dec 8.

Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands.

Since its creation in 2002, the European Food Safety Authority (EFSA) has produced risk assessments for over 5000 substances in >2000 Scientific Opinions, Statements and Conclusions through the work of its Scientific Panels, Units and Scientific Committee. OpenFoodTox is an open source toxicological database, available both for download and data visualisation which provides data for all substances evaluated by EFSA including substance characterisation, links to EFSA's outputs, applicable legislations regulations, and a summary of hazard identification and hazard characterisation data for human health, animal health and ecological assessments. The database has been structured using OECD harmonised templates for reporting chemical test summaries (OHTs) to facilitate data sharing with stakeholders with an interest in chemical risk assessment, such as sister agencies, international scientific advisory bodies, and others. This manuscript provides a description of OpenFoodTox including data model, content and tools to download and search the database. Examples of applications of OpenFoodTox in chemical risk assessment are discussed including new quantitative structure-activity relationship (QSAR) models, integration into tools (OECD QSAR Toolbox and AMBIT-2.0), assessment of environmental footprints and testing of threshold of toxicological concern (TTC) values for food related compounds. Finally, future developments for OpenFoodTox 2.0 include the integration of new properties, such as physico-chemical properties, exposure data, toxicokinetic information; and the future integration within in silico modelling platforms such as QSAR models and physiologically-based kinetic models. Such structured in vivo, in vitro and in silico hazard data provide different lines of evidence which can be assembled, weighed and integrated using harmonised Weight of Evidence approaches to support the use of New Approach Methodologies (NAMs) in chemical risk assessment and the reduction of animal testing.
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http://dx.doi.org/10.1016/j.envint.2020.106293DOI Listing
January 2021

Prediction of No Observed Adverse Effect Concentration for inhalation toxicity using Monte Carlo approach.

SAR QSAR Environ Res 2020 Dec 12;31(12):1-12. Epub 2020 Nov 12.

Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS , Milano, Italy.

Ideal correlation is one variable model based on so-called optimal descriptors calculated with simplified molecular input-line entry systems (SMILES). The optimal descriptor is calculated according to the index of ideality of correlation, a new criterion of predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs). The aim of the present study was the building and estimation of models for inhalation toxicity as No Observed Adverse Effect Concentration (NOAEC) based on the OECD guidelines 413. Three random distributions into the training set and validation set were examined. In practice, a structured training set that contains active training set, passive training set and calibration set is used as the training set. The statistical characteristics of the best model for negative logarithm of NOAEC (pNOAEC) are for training set = 108, average  = 0.52 + 0.62 + 0.76/3 = 0.63 and for validation set = 35,  = 0.73.
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http://dx.doi.org/10.1080/1062936X.2020.1841827DOI Listing
December 2020

How the CORAL software can be used to select compounds for efficient treatment of neurodegenerative diseases?

Toxicol Appl Pharmacol 2020 12 13;408:115276. Epub 2020 Oct 13.

Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA. Electronic address:

Recommendations on the efficient application of CORAL software (http://www.insilico.eu/coral) to establish quantitative structure-property/activity relationships (QSPRs/QSARs) are provided. The predictive potential of the approach has been demonstrated for QSAR models developed for inhibitor concentrations (negative decimal logarithm of IC50) of derivatives of N-methyl-d-aspartate (NMDA) receptor, leucine-rich repeat kinase 2 (LRRK2), and tropomyosin receptor kinase A (TrkA). The above three protein targets are related to various neurodegenerative diseases such as Alzheimer's and Parkinson's. Each model was checked using several splits of the data for the training and the validation sets. The index of ideality of correlation (IIC) represents a tool to improve the predictive potential for an arbitrary model. However, the use of the IIC should be carried out according to rules, described in this work.
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http://dx.doi.org/10.1016/j.taap.2020.115276DOI Listing
December 2020

SARS-CoV M inhibitory activity of aromatic disulfide compounds: QSAR model.

J Biomol Struct Dyn 2020 Sep 9:1-7. Epub 2020 Sep 9.

Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS, USA.

The main protease (M) of SARS-associated coronavirus (SARS-CoV) had caused a high rate of mortality in 2003. Current events (2019-2020) substantiate important challenges for society due to coronaviruses. Consequently, advancing models for the antiviral activity of therapeutic agents is a necessary component of the fast development of treatment for the virus. An analogy between anti-SARS agents suggested in 2017 and anti-coronavirus COVID-19 agents are quite probable. Quantitative structure-activity relationships for SARS-CoV are developed and proposed in this study. The statistical quality of these models is quite good. Mechanistic interpretation of developed models is based on the statistical and probability quality of molecular alerts extracted from SMILES. The novel, designed structures of molecules able to possess anti-SARS activities are suggested. For the final assessment of the designed molecules inhibitory potential, developed from the obtained QSAR model, molecular docking studies were applied. Results obtained from molecular docking studies were in a good correlation with the results obtained from QSAR modeling.
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http://dx.doi.org/10.1080/07391102.2020.1818627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544941PMC
September 2020

Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2.

Comb Chem High Throughput Screen 2020 Sep 2. Epub 2020 Sep 2.

Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700. Thailand.

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors.

Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles.

Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets.

Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors.

Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.
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http://dx.doi.org/10.2174/1386207323666200902141907DOI Listing
September 2020

Correlation intensity index: mathematical modeling of cytotoxicity of metal oxide nanoparticles.

Nanotoxicology 2020 10 2;14(8):1118-1126. Epub 2020 Sep 2.

Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.

Metal oxide nanoparticles (MO-NPs) have unique structural characteristics, exceptionally high surface area, strong mechanical stability, catalytic activities, and are biocompatible. Consequently, MO-NPs have recently attracted considerable interest in the field of imaging-guided therapeutic and biosensing applications. This study aims to develop Quantitative Structure-Activity Relationships (QSAR) for the prediction of cell viability of MO-NPs. The QSAR model based on the so-called optimal descriptors which calculated with a simplified molecular input-line entry system (SMILES). The Monte Carlo technique applied to calculate correlation weights for SMILES fragments. Factually, the optimal descriptor for SMILES is the summation of the correlation weights. The model of cytotoxicity is one variable correlation between cytotoxicity and the above optimal descriptor. The Correlation Intensity Index (CII) is a possible criterion of the predictive potential of the model. Applying the CII as a component of the target function in the Monte Carlo optimization routine, employed by the CORAL program, that is designed to find a predictive relationship between the optimal descriptor and cytotoxicity of MO-NPs, improves the statistical quality of the model. The significance of different eclectic features, in terms of whether they increase/decrease cell viability, i.e. decrease or increase cytotoxicity, is also discussed. Numerical data on 83 experimental samples of MO-NPs activity under different conditions taken from the literature are applied for the "nano-QSAR" analysis.
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http://dx.doi.org/10.1080/17435390.2020.1808252DOI Listing
October 2020

QSAR model for pesticides toxicity to Rainbow Trout based on "ideal correlations".

Aquat Toxicol 2020 Oct 9;227:105589. Epub 2020 Aug 9.

Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.

Pesticides have an impact on the aquatic environment, with ecological effects. The regulation of this impact is of key importance. One of the components of the planning of agricultural and industrial activities is the development of databases and models in order to identify substances that may cause damage. In this study, a quantitative structure-activity relationship (QSAR) approach was established for the prediction of acute toxicity toward rainbow trout of various pesticides. The so-called index of ideality of correlation is the main component of this approach. The validation of this approach has been carried out with three random splits into the training and validation sets. The range of statistical quality of models obtained here for the validation set is R = [0.81-0.86] and RMSE = [0.55-0.65].
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http://dx.doi.org/10.1016/j.aquatox.2020.105589DOI Listing
October 2020

Zebrafish AC modelling: (Q)SAR models to predict developmental toxicity in zebrafish embryo.

Ecotoxicol Environ Saf 2020 Oct 11;202:110936. Epub 2020 Jul 11.

Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Department of Environmental Health Sciences, Laboratory of Environmental Toxicology, Via Mario Negri 2, 20156, Milan, Italy.

Developmental toxicity refers to the occurrence of adverse effects on a developing organism as a consequence of exposure to hazardous chemicals. The assessment of developmental toxicity has become relevant to the safety assessment process of chemicals. The zebrafish embryo developmental toxicology assay is an emerging test used to screen the teratogenic potential of chemicals and it is proposed as a promising test to replace teratogenic assays with animals. Supported by the increased availability of data from this test, the developmental toxicity assay with zebrafish has become an interesting endpoint for the in silico modelling. The purpose of this study was to build up quantitative structure-activity relationship (QSAR) models. In this work, new in silico models for the evaluation of developmental toxicity were built using a well-defined set of data from the ToxCast Phase I chemical library on the zebrafish embryo. Categorical and continuous QSAR models were built by gradient boosting machine learning and the Monte Carlo technique respectively, in accordance with Organization for Economic Co-operation and Development principles and their statistical quality was satisfactory. The classification model reached balanced accuracy 0.89 and Matthews correlation coefficient 0.77 on the test set. The regression model reached correlation coefficient R 0.70 in external validation and leave-one-out cross-validated Q 0.73 in internal validation.
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http://dx.doi.org/10.1016/j.ecoenv.2020.110936DOI Listing
October 2020

Ecosystem ecology: Models for acute toxicity of pesticides towards Daphnia magna.

Environ Toxicol Pharmacol 2020 Nov 25;80:103459. Epub 2020 Jul 25.

Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.

Quantitative structure - activity relationships (QSARs) which are obtained with a representation of the molecular architecture via simplified molecular input-line entry system (SMILES) are applied to build up predictive models of acute toxicity of pesticides towards Daphnia magna. The acute toxicity towards Daphnia magna is an adequate measure of the ecological impact of various substances. The Monte Carlo technique is the basis to build up the above QSAR models. The statistical quality of suggested models is good: the best model is characterized by n = 103, R = 0.76, RMSE = 0.91 (training set); n = 53, R = 0.82, RMSE = 0.87 (validation set). The approach provides the mechanistic interpretation (e.g. aromaticity and branching of carbon skeleton are promoters of increase for toxicity towards Daphnia magna in the case of the examined set of pesticides). The approach is attractive to build up predictive models since instead of a large number of different molecular descriptors the corresponding model is based on solely one optimal descriptor calculated with SMILES and all necessary calculations can be done using the CORAL software available on the Internet (http://ww.insilico.eu/coral).
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http://dx.doi.org/10.1016/j.etap.2020.103459DOI Listing
November 2020

'Ideal correlations' for the predictive toxicity to .

Toxicol Mech Methods 2020 Oct 14;30(8):605-610. Epub 2020 Aug 14.

Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.

Objectives: Predictive models for toxicity to are an important component of natural sciences. The present study aims to build up a predictive model for the endpoint using the so-called index of ideality of correlation (). Besides, the comparison of the predictive potential of these models with the predictive potential of models suggested in the literature is the task of the present study.

Methods: The Monte Carlo technique is a tool to build up the predictive model applied in this study. The molecular structure is represented via a simplified molecular input-line entry system (SMILES). The is a statistical characteristic sensitive to both the correlation coefficient and mean absolute error. Applying of the to build up quantitative structure-activity relationships (QSARs) for the toxicity to improves the predictive potential of those models for random splits into the training set and the validation set. The calculation was carried out with CORAL software (http://www.insilico.eu/coral).

Results: The statistical quality of the suggested models is incredibly good for the external validation set, but the statistical quality of the models for the training set is modest. This is the paradox of ideal correlation, which is obtained with applying the

Conclusions: The Monte Carlo technique is a convenient and reliable way to build up a predictive model for toxicity to . The is a useful statistical criterion for building up predictive models as well as for the assessment of their statistical quality.
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http://dx.doi.org/10.1080/15376516.2020.1801928DOI Listing
October 2020

Integrated Models for the Prediction of No-Observed-(Adverse)-Effect Levels and Lowest-Observed-(Adverse)-Effect Levels in Rats for Sub-chronic Repeated-Dose Toxicity.

Chem Res Toxicol 2021 Feb 4;34(2):247-257. Epub 2020 Aug 4.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milan, Italy.

Repeated-dose toxicity (RDT) is a critical endpoint for hazard characterization of chemicals and is assessed to derive safe levels of exposure for human health. Here we present the first attempt to model simultaneously no-observed-(adverse)-effect level (NO(A)EL) and lowest-observed-(adverse)-effect level (LO(A)EL). Classification and regression models were derived based on rat sub-chronic repeated dose toxicity data for 327 compounds from the Fraunhofer RepDose database. Multi-category classification models were built for both NO(A)EL and LO(A)EL though a consensus of statistics- and fragment-based algorithms, while regression models were based on quantitative relationships between the endpoints and SMILES-based attributes. NO(A)EL and LO(A)EL models were integrated, and predictions were compared to exclude inconsistent values. This strategy improved the performance of single models, leading to greater than 0.70, root-mean-square error (RMSE) lower than 0.60 (for regression models), and accuracy of 0.61-0.73 (for classification models) on the validation set, based on the endpoint and the threshold applied for selecting predictions. This study confirms the effectiveness of the modeling strategy presented here for assessing RDT of chemicals using models.
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http://dx.doi.org/10.1021/acs.chemrestox.0c00176DOI Listing
February 2021

Medicinal Chemistry and Computational Chemistry: Mutual Influence and Harmonization.

Authors:
Alla P Toropova

Mini Rev Med Chem 2020 ;20(14):1320-1321

Laboratory of Environmental Chemistry and Toxicology Istituto di Ricerche Farmacologiche Mario Negri IRCCS Via Mario Negri 2, 20156 Milano, Italy.

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http://dx.doi.org/10.2174/138955752014200626163614DOI Listing
January 2020

Correlation intensity index: Building up models for mutagenicity of silver nanoparticles.

Sci Total Environ 2020 Oct 27;737:139720. Epub 2020 May 27.

Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy. Electronic address:

Nanomaterials become significant component of economics. Consequently, nanomaterials become object of environmental sciences. There is a traditional list of endpoints which are indicators of the ecological risk. Mutagenicity is one of important component in this list. The quasi-SMILES approach, that in contrast to majority of work dedicated to modelling behaviour of nanomaterials gives possibility to consider experimental conditions as well as other circumstances which can impact the behaviour of nanomaterials is suggested. This is carried out via so-called quasi-SMILES. The quasi-SMILES is a line on of codes that contains all the above available eclectic data. Modelling process aimed to build up a model involves Correlation Intensity Index (CII) that is a new criterion of predictive potential of models. The scheme of calculation of CII is described in this work in the first time. The applying of CII together with Index of Ideality Correlation (IIC) in modelling of mutagenicity of silver nanoparticles by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral) indicates that application of the CII improves the predictive potential of these models for three random splits into the training set (75%) and validation set (25%).
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http://dx.doi.org/10.1016/j.scitotenv.2020.139720DOI Listing
October 2020

Surface Morphology Formation of Edible Holographic Marker on Potato Starch with Gelatin or Agar Thin Coatings.

Polymers (Basel) 2020 May 14;12(5). Epub 2020 May 14.

Bioengineering Research Center, ITMO University, Kronverkskiy Prospekt 49, 197101 Saint Petersburg, Russia.

Edible films and coatings based on biopolymers to protect and extend the shelf life of food and medicine can be functionalized, by applying a holographic marker on the coating surface for marking products or sensing storage conditions. In this work, holographic markers were prepared on the surface of thin biopolymer coatings based on starch, gelatin, agar and also starch/gelatin and starch/agar blends by the nanoimprint method from a film-forming solution. The morphology of the surface of holographic markers using optical microscopy in reflection mode was examined, as well as the reasons for its formation using an analysis of the flow curves of film-forming solutions. It was found that the surface morphology of the marker strongly depends on the composition, consistency index of film-forming solution and miscibility of the components. It was shown that the starch/agar film-forming solution at the ratio of 70/30 wt.% has a low consistency index value of 21.38 Pa·s, compared to 64.56 Pa·s for pure starch at a drying temperature of 30 °C, and the components are well compatible. Thus, an isotropic morphology of the holographic marker surface was formed and the value of diffraction efficiency of 3% was achieved, compared to 1.5% for the marker made of pure starch. Coatings without holographic markers were analyzed by tensile strength and water contact angle, and their properties are highly dependent on their composition.
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http://dx.doi.org/10.3390/polym12051123DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284560PMC
May 2020

Pesticides, cosmetics, drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity.

Mol Divers 2020 Apr 23. Epub 2020 Apr 23.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy.

The similarity is an important category in natural sciences. A measure of similarity for a group of various biochemical endpoints is suggested. The list of examined endpoints contains (1) toxicity of pesticides towards rainbow trout; (2) human skin sensitization; (3) mutagenicity; (4) toxicity of psychotropic drugs; and (5) anti HIV activity. Further applying and evolution of the suggested approach is discussed. In particular, the conception of the similarity (dissimilarity) of endpoints can play the role of a "useful bridge" between quantitative structure property/activity relationships (QSPRs/QSARs) and read-across technique.
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http://dx.doi.org/10.1007/s11030-020-10085-3DOI Listing
April 2020

QSPR/QSAR: State-of-Art, Weirdness, the Future.

Molecules 2020 Mar 12;25(6). Epub 2020 Mar 12.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.

Ability of quantitative structure-property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the "statistical similarity" of different endpoints is suggested and illustrated by an example for relatively "distanced each from other" endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood-brain barrier.
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http://dx.doi.org/10.3390/molecules25061292DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7143984PMC
March 2020

Science, Medicine and the Creation of a 'Healthy' Soviet Cinema.

Authors:
Anna Toropova

J Contemp Hist 2020 Jan 27;55(1):3-28. Epub 2019 Mar 27.

University of Nottingham, UK.

Cinema had long been hailed by Bolshevik party leaders as a crucial ally of the Soviet mass enlightenment project. By the mid-1920s, however, Soviet psychologists, educators and practitioners of 'child science' (pedology) were pointing to the grave effects that the consumption of commercial cinema was exerting on the physical, mental and moral health of Soviet young people. Diagnosing an epidemic of 'film mania', specialists battled to curtail the NEP-era practices of film production and demonstration that had rendered cinema 'toxic' to children. Campaigns to 'healthify' Soviet cinema, first manifesting in the organization of child-friendly screenings and forms of 'cultural enlightenment work', soon extended to attempts to develop a new children's film repertoire based on the results of psycho-physiological viewer studies. A vast variety of pedological research institutions established during the late 1920s and early 1930s began to experimentally test cinema's effects on children with the view of assisting the production of films that could cultivate a sound mind and body. Tracing a link between the findings of pedological viewer studies and the 'healthy' cinema championed in the 1930s, this article sheds light on the vital role played by medical and scientific expertise in shaping Stalinist culture.
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http://dx.doi.org/10.1177/0022009418820111DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7000851PMC
January 2020

The using of the Index of Ideality of Correlation (IIC) to improve predictive potential of models of water solubility for pesticides.

Environ Sci Pollut Res Int 2020 Apr 4;27(12):13339-13347. Epub 2020 Feb 4.

Scientific Committee and Emerging Risks Unit, European Food Safety Authority, Via Carlo Magno 1A, 43126, Parma, Italy.

Models for water solubility of pesticides suggested in this manuscript are important data from point of view of ecologic engineering. The Index of Ideality of Correlation (IIC) of groups of quantitative structure-property relationships (QSPRs) for water solubility of pesticides related to the calibration sets was used to identify good in silico models. This comparison confirmed the high IIC set provides better statistical quality of the model for the validation set. Though there are large databases on solubility, the reliable prediction of the endpoint for new substances which are potential pesticides is an important ecologic task. Unfortunately, predictive models for various endpoints suffer overtraining, and the IIC serves to avoid or at least reduce this. Thus, the approach suggested has both theoretical and economic effects for ecology.
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http://dx.doi.org/10.1007/s11356-020-07820-6DOI Listing
April 2020

The Use of the Index of Ideality of Correlation to Build Up Models for Bioconcentration Factor.

Mol Inform 2020 07 13;39(7):e1900070. Epub 2020 Mar 13.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156, Milano, Italy.

We establish a QSPR analysis for the bioconcentration factor of 851 heterogeneous structural compounds. Linear models are proposed via two different approaches: i. the optimal descriptor method implemented in CORAL, and ii. multivariable linear regressions on the best molecular descriptors found with the Replacement Method on 44,216 structural descriptors. Such variables are derived with different freely available softwares, such as PaDEL, PyDescriptor, Mold , QuBiLs-MAS and ISIDA/Fragmentor. The same validation set is employed in order to compare the predictive performance between the so-obtained CORAL and RM based models. Finally, the comparison of several models for the bioconcentration factor confirms the ability of the so-called index of ideality of correlation to be a criterion of predictive potential in Quantitative Structure-Property Relationships.
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http://dx.doi.org/10.1002/minf.201900070DOI Listing
July 2020

QSAR models for biocides: The example of the prediction of acute toxicity.

SAR QSAR Environ Res 2020 Mar 16;31(3):227-243. Epub 2020 Jan 16.

Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Milano, Italy.

Biocides are multi-component products used to control undesired and harmful organisms able to affect human or animal health or to damage natural and manufactured products. Because of their widespread use, aquatic and terrestrial ecosystems could be contaminated by biocides. The environmental impact of biocides is evaluated through eco-toxicological studies with model organisms of terrestrial and aquatic ecosystems. We focused on the development of in silico models for the evaluation of the acute toxicity (EC) of a set of biocides collected from different sources on the freshwater crustacean , one of the most widely used model organisms in aquatic toxicology. Toxicological data specific for biocides are limited, so we developed three models for daphnid toxicity using different strategies (linear regression, random forest, Monte Carlo (CORAL)) to overcome this limitation. All models gave satisfactory results in our datasets: the random forest model showed the best results with a determination coefficient = 0.97 and 0.89, respectively, for the training (TS) and the validation sets (VS) while linear regression model and the CORAL model had similar but lower performance ( = 0.83 and 0.75, respectively, for TS and VS in the linear regression model and = 0.74 and 0.75 for the CORAL model).
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http://dx.doi.org/10.1080/1062936X.2019.1709221DOI Listing
March 2020

Use of the index of ideality of correlation to improve aquatic solubility model.

J Mol Graph Model 2020 05 28;96:107525. Epub 2019 Dec 28.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156, Milano, Italy.

Models for water solubility are built up using so-called correlation weights of fragments of simplified molecular input-line entry system (SMILES), 2D graph invariants, and hierarchy of rings present in molecules. The approach is based on Monte Carlo optimization of the molecular features. Two versions of the optimization were examined: (i) the traditional version; and (ii) the Index of Ideality of Correlation (IIC) version. The statistical quality of models obtained with use of the IIC is comparable with models built up with application of physico-chemical endpoints and quantum mechanics descriptors recently suggested in the literature.
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http://dx.doi.org/10.1016/j.jmgm.2019.107525DOI Listing
May 2020

Predicting acute contact toxicity of organic binary mixtures in honey bees (A. mellifera) through innovative QSAR models.

Sci Total Environ 2020 Feb 19;704:135302. Epub 2019 Nov 19.

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via la Masa 19, 20156 Milan, Italy.

Pollinators such as honey bees are of considerable importance, because of the crucial pollination services they provide for food crops and wild plants. Since bees are exposed to a wide range of multiple chemicals "mixtures" both of anthropogenic (e.g. plant protection products) and natural origin (e.g. plant toxins), understanding their combined toxicity is critical. Although honey bees are employed worldwide as surrogate species for Apis and non-Apis bees in toxicity tests, it is practically unfeasible to perform in vivo tests for all mixtures of chemicals. Therefore, Quantitative Structure-Activity Relationships (QSAR) models can be developed using available data and can provide useful tools to predict such combined toxicity. Here, three different QSAR models within the CORAL software have been calibrated and validated for honey bees (A. mellifera) to predict the acute contact mixtures potency (LD), in two regression based-models, and the nature of combined toxicity (synergism / non-synergism) in a classification-based model. Experimental data on binary mixtures (n = 123) (LD) including dose response data (n = 97) and corresponding Toxic Unit values were retrieved from EFSA databases. The models were built using the principle of extraction of attributes from SMILES (or quasi-SMILES) while calculating so-called correlation weights for these attributes using Monte Carlo techniques. The two regression models were validated for their reliability and robustness (R = 0.89, CCC = 0.92, Q = 0.81; R = 0.87, CCC = 0.89, Q = 0.75). The classification model was validated using sensitivity (=0.86), specificity (=1), accuracy (=0.96), and Matthews correlation coefficient (MCC = 0.90) as qualitative statistical validation parameters. Results indicate that these QSAR models successfully predict acute contact toxicity of binary mixtures in honey bees and can support prioritisation of multiple chemicals of concerns. Data gaps and further development of QSAR models for honey bees are highlighted particularly for chronic and sub-lethal effects.
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http://dx.doi.org/10.1016/j.scitotenv.2019.135302DOI Listing
February 2020

Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

Curr Top Med Chem 2019 ;19(29):2643-2657

Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milano, Italy.

Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.
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http://dx.doi.org/10.2174/1568026619666191105111817DOI Listing
February 2020

CORAL: Building up QSAR models for the chromosome aberration test.

Saudi J Biol Sci 2019 Sep 9;26(6):1101-1106. Epub 2018 May 9.

Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy.

A high level of chromosomal aberrations in peripheral blood lymphocytes may be an early marker of cancer risk, but data on risk of specific cancers and types of chromosomal aberrations are limited. Consequently, the development of predictive models for chromosomal aberrations test is important task. Majority of models for chromosomal aberrations test are so-called knowledge-based rules system. The CORAL software (http://www.insilico.eu/coral, abbreviation of "CORrelation And Logic") is an alternative for knowledge-based rules system. In contrast to knowledge-based rules system, the CORAL software gives possibility to estimate the influence upon the predictive potential of a model of different molecular alerts as well as different splits into the training set and validation set. This possibility is not available for the approaches based on the knowledge-based rules system. Quantitative Structure-Activity Relationships (QSAR) for chromosome aberration test are established for five random splits into the training, calibration, and validation sets. The QSAR approach is based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) without data on physicochemical and/or biochemical parameters. In spite of this limitation, the statistical quality of these models is quite good.
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http://dx.doi.org/10.1016/j.sjbs.2018.05.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6734133PMC
September 2019

Corrigendum to "CORAL: Binary classifications (active/inactive) for drug-induced liver injury" [Toxicol. Lett. 268 (2017) (February) 51-57].

Toxicol Lett 2019 Oct 26;313:205. Epub 2019 Jun 26.

Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156, Milano, Italy. Electronic address:

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http://dx.doi.org/10.1016/j.toxlet.2019.04.023DOI Listing
October 2019